


'''simple docstring'''


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def      snake_case_  (    SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None	):







				"""simple docstring"""





				return field(default_factory=lambda: default				, metadata=SCREAMING_SNAKE_CASE__	)





@dataclass
class        lowercase__    :





							'''simple docstring'''


							A_				: str       =    field(
							    metadata={"""help""": """The csv file to plot."""}	,	)
							A_				: bool       =    field(
							    default=_snake_case	,	metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}	,	)
							A_				: bool       =    field(
							    default=_snake_case	,	metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}	,	)
							A_				: bool       =    field(
							    default=_snake_case	,	metadata={"""help""": """Disable logarithmic scale when plotting"""}	,	)
							A_				: bool       =    field(
							    default=_snake_case	,	metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }	,	)
							A_				: Optional[str]       =    field(
							    default=_snake_case	,	metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}	,	)
							A_				: Optional[List[str]]       =    list_field(
							    default=_snake_case	,	metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}   )







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				try:
								int(SCREAMING_SNAKE_CASE__	)
								return True
				except ValueError:
								return False







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				try:
								float(SCREAMING_SNAKE_CASE__	)
								return True
				except ValueError:
								return False





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= args
											_SCREAMING_SNAKE_CASE					:			List[Any]							= defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

											with open(self.args.csv_file       ,   newline=""""""    ) as csv_file:
															_SCREAMING_SNAKE_CASE					:			str							= csv.DictReader(__snake_case    )
															for row in reader:
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= row["""model"""]
																			self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]    )    )
																			self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]    )    )
																			if can_convert_to_int(row["""result"""]    ):
																							# value is not None
																							_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= int(row["""result"""]    )
																			elif can_convert_to_float(row["""result"""]    ):
																							# value is not None
																							_SCREAMING_SNAKE_CASE					:			int							= float(row["""result"""]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= plt.subplots()
											_SCREAMING_SNAKE_CASE					:			Dict							= """Time usage""" if self.args.is_time else """Memory usage"""
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

											if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale("""log"""    )
															ax.set_yscale("""log"""    )

											for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()    )

											for model_name_idx, model_name in enumerate(self.result_dict.keys()    ):
															_SCREAMING_SNAKE_CASE					:			str							= sorted(set(self.result_dict[model_name]["""bsz"""]    )    )
															_SCREAMING_SNAKE_CASE					:			str							= sorted(set(self.result_dict[model_name]["""seq_len"""]    )    )
															_SCREAMING_SNAKE_CASE					:			Tuple							= self.result_dict[model_name]["""result"""]

															((_SCREAMING_SNAKE_CASE)						,			(_SCREAMING_SNAKE_CASE))					:			Optional[Any]							= (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															_SCREAMING_SNAKE_CASE					:			Tuple							= (
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																			if self.args.plot_along_batch:
																							_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,   dtype=__snake_case       ,   )
																			else:
																							_SCREAMING_SNAKE_CASE					:			Dict							= np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,   dtype=np.floataa       ,   )

																			((_SCREAMING_SNAKE_CASE)						,			(_SCREAMING_SNAKE_CASE))					:			Tuple							= (
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			_SCREAMING_SNAKE_CASE					:			int							= np.asarray(__snake_case       ,   __snake_case    )[: len(__snake_case    )]
																			plt.scatter(
																			    __snake_case       ,   __snake_case       ,   label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""    )
																			plt.plot(__snake_case       ,   __snake_case       ,   """--"""    )

															title_str += f""" {label_model_name} vs."""

											_SCREAMING_SNAKE_CASE					:			Dict							= title_str[:-4]
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """Time in s""" if self.args.is_time else """Memory in MB"""

											# plot
											plt.title(__snake_case    )
											plt.xlabel(__snake_case    )
											plt.ylabel(__snake_case    )
											plt.legend()

											if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file    )
											else:
															plt.show()







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= HfArgumentParser(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= Plot(args=SCREAMING_SNAKE_CASE__	)
				plot.plot()


if __name__ == "__main__":
				main()








'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				print("""\nThe shortest path matrix using Floyd Warshall algorithm\n"""	)
				for i in range(SCREAMING_SNAKE_CASE__	):
								for j in range(SCREAMING_SNAKE_CASE__	):
												if dist[i][j] != float("""inf"""	):
																print(int(dist[i][j]	)				, end="""\t"""	)
												else:
																print("""INF"""				, end="""\t"""	)
								print()







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= [[float("""inf"""	) for _ in range(SCREAMING_SNAKE_CASE__	)] for _ in range(SCREAMING_SNAKE_CASE__	)]

				for i in range(SCREAMING_SNAKE_CASE__	):
								for j in range(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			List[str]							= graph[i][j]

												# check vertex k against all other vertices (i, j)
				for k in range(SCREAMING_SNAKE_CASE__	):
								# looping through rows of graph array
								for i in range(SCREAMING_SNAKE_CASE__	):
												# looping through columns of graph array
												for j in range(SCREAMING_SNAKE_CASE__	):
																if (
																    dist[i][k] != float("""inf"""	)
																    and dist[k][j] != float("""inf"""	)
																    and dist[i][k] + dist[k][j] < dist[i][j]
																):
																				_SCREAMING_SNAKE_CASE					:			int							= dist[i][k] + dist[k][j]

				_print_dist(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return dist, v


if __name__ == "__main__":
				UpperCAmelCase_						:  str				  =						int(input('Enter number of vertices: '))
				UpperCAmelCase_						:  Union[str, Any]				  =						int(input('Enter number of edges: '))

				UpperCAmelCase_						:  Dict				  =						[[float('inf') for i in range(v)] for j in range(v)]

				for i in range(v):
								UpperCAmelCase_						:  Union[str, Any]				  =						0.0

								# src and dst are indices that must be within the array size graph[e][v]
								# failure to follow this will result in an error
				for i in range(e):
								print('\nEdge ', i + 1)
								UpperCAmelCase_						:  int				  =						int(input('Enter source:'))
								UpperCAmelCase_						:  List[str]				  =						int(input('Enter destination:'))
								UpperCAmelCase_						:  List[str]				  =						float(input('Enter weight:'))
								UpperCAmelCase_						:  int				  =						weight

				floyd_warshall(graph, v)

				# Example Input
				# Enter number of vertices: 3
				# Enter number of edges: 2

				# # generated graph from vertex and edge inputs
				# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
				# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

				# specify source, destination and weight for edge #1
				# Edge  1
				# Enter source:1
				# Enter destination:2
				# Enter weight:2

				# specify source, destination and weight for edge #2
				# Edge  2
				# Enter source:2
				# Enter destination:1
				# Enter weight:1

				# # Expected Output from the vertice, edge and src, dst, weight inputs!!
				# 0		INF	INF
				# INF	0	2
				# INF	1	0




